Improving N calculation of the RSI financial indicator using neural networks

Proceeding of: 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE 2010), 17-19 September 2010, Chongqing, China 2010Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing large financial datasets and have become in the current economic landscape a significant challenge for multi disciplinary research. Particularly, Trading oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of evolutionary computing which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof of concept architecture and implementation of a Trading Decision Support System based on the RSI N value calculation and Feed Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the N calculation for RSI and a more precise and improved upshot obtained from feed forward algorithms application to stock value datasets.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project GODO2 (TSI- 020100-2008-564) and SONAR2 (TSI-020100-2008- 665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02). Furthermore, this work is supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work is sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEPPublicad

Toward integration of knowledge based systems and knowledge discovery systems

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the technologies involved

Toward integration of knowledge based systems and knowledge discovery systems

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the technologies involved.Facultad de Informátic

Toward integration of knowledge based systems and knowledge discovery systems

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the technologies involved.Facultad de Informátic

Improving trading saystems using the RSI financial indicator and neural networks.

Proceedings of: 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010), 20 August-3 September 2010, Daegu (Korea)Trading and Stock Behavioral Analysis Systems require efficient Artificial Intelligence techniques for analyzing Large Financial Datasets (LFD) and have become in the current economic landscape a significant challenge for multi-disciplinary research. Particularly, Trading-oriented Decision Support Systems based on the Chartist or Technical Analysis Relative Strength Indicator (RSI) have been published and used worldwide. However, its combination with Neural Networks as a branch of computational intelligence which can outperform previous results remain a relevant approach which has not deserved enough attention. In this paper, we present the Chartist Analysis Platform for Trading (CAST, in short) platform, a proof-of-concept architecture and implementation of a Trading Decision Support System based on the RSI and Feed-Forward Neural Networks (FFNN). CAST provides a set of relatively more accurate financial decisions yielded by the combination of Artificial Intelligence techniques to the RSI calculation and a more precise and improved upshot obtained from feed-forward algorithms application to stock value datasets.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665 and GO2 (TSI-020400-2009-127). Furthermore, this work is supported by the General Council of Superior Technological Education of Mexico (DGEST). Additionally, this work is sponsored by the National Council of Science and Technology (CONACYT) and the Public Education Secretary (SEP) through PROMEP.Publicad

Hacia una propuesta integradora de sistemas basados en conocimiento y de descubrimiento

This paper proposes a system architecture for integrating knowledge discovery and knowledge based systems.\nSome considerations on the development viability of the associated system are drawn based on the involved technologies maturity.En este trabajo se formula una propuesta de arquitectura de integración entre sistemas de descubrimiento de conocimiento (adquisición automática) y sistemas basados en conocimiento (sistemas expertos). Se formulan consideraciones sobre la viabilidad de implementación de dicha arquitectura en función de la madurez de las tecnologías involucradas.III Workshop de Ingeniería de Software y Bases de Datos (WISBD

Toward integration of knowledge based systems and knowledge discovery systems

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advance of the technologies involved.Facultad de Informátic